It is well known that DL-based methods can be divided into supervised and unsupervised methods. This is the simplest classification criterion, but the range of this classification criterion is too large and not clear. As a result, many technically unrelated methods may be classified into the same type while methods with similar strategies may be classified into completely different types. Different from previous SISR surveys [1]}, [2]} that use supervision as the classification criterion or introduce the methods in a pure literature way, in this survey, we attempt to give a comprehensive overview of DL-based SISR methods and categorize them according to their specific targets. In Fig. REF , we show the content and taxonomy of this survey. Obviously, we divide the DL-based SISR methods into four categories: reconstruction efficiency methods, reconstruction accuracy methods, perceptual quality methods, and further improvement methods. This target-based survey has a clear context hence it is convenient for readers to consult. Specifically, in this survey, we first introduce the problem definition, research background, and significance of SISR. Then, we introduce some related works, including benchmark datasets, upsample methods, optimization objectives, and assessment methods. After that, we provide a detailed investigation of SISR methods and provide the reconstruction results of them. Finally, we discuss some issues that still exist in SISR and provide some new trends and future directions. Overall, the main contributions of this survey are as follows:
